Accelerating Formulation Design via Machine Learning: Generating a High-throughput Shampoo Formulations Dataset.

ABSTRACT: Liquid formulations are ubiquitous yet have lengthy product development cycles owing to the complex physical interactions between ingredients making it difficult to tune formulations to customer-defined property targets. Interpolative ML models can accelerate liquid formulations design but are typically trained on limited sets of ingredients and without any structural information, which limits their out-of-training predictive capacity. To address this challenge, we selected eighteen formulation ingredients covering a diverse chemical space to prepare an open experimental dataset for training ML models for rinse-off formulations development. The resulting design space has an over 50-fold increase in dimensionality compared to our previous work. Here, we present a dataset of 812 formulations

SUBMITTER: Chitre A 

PROVIDER: S-EPMC11222379 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature